A sizable percentage of abnormalities in mammograms consists of microcalcifications (MCCs) that are tiny deposits (spots) of calcium in breast carcinoma and that account for up to 50% of the diagnosed cases. It is also recognized that MCC clusters are early signs of breast cancer. Various computer aided diagnosis (CAD) systems have been developed to help radiologists in making decisions concerning follow up and biopsies, applying pattern classifiers to identifiable features either of MCC spots or of background (for example, A. Karahaliou, S. Skiadopoulos, I. Boniatis, P. Sakellaropoulos, E. Likaki, G. Panayiotakis and L. Costaridou, “Texture analysis of tissue surrounding MCCs on mammograms for breast cancer diagnosis,” The British J Radiology, 80, 648-656 (2007)).
“Single-stage” classifiers such as rule-based systems, fuzzy logic systems, support vector machines and, overwhelmingly, neural networks (NN), are reported in the literature for the use of MCC classification (A. Papadopoulos, D. Fotiadis and A. Likas, “An automatic microcalcification detection system based on a hybrid neural network classifier,” Artificial Intelligence in Medicine, 25, 149-167 (2002)). Papadopoulos et al., in their MCC detection algorithm, use a rule-based sub-system that removes false positives while retaining majority true positives, followed by an NN sub-system that determines the final classification performance. The category of ‘hybrid’ classifier can also be found in other research areas such as face detection with cascading classifiers (see P. Voila and M. Jones, “Robust real-time object detection,” 2nd Int. Workshop on Statistical and Computational Theory of Vision, 1-25 (2001)). In Voila and Jones' implementation, the cascade classifier consists of a number of independent, discriminating modules called “strong classifiers” that are trained to discriminate between object and non-object windows by examining specific image measurements from an object candidate window supplied by a list of objects. In the context of the present invention, so-called ‘hybrid’ classifiers or cascade classifiers are considered single-stage classifiers as opposed to the multistage classifier that is to be addressed subsequently.
To the diagnostician, it is desired that MCCs be found early so that cancer mortality can be reduced. With this requirement in mind, researchers have developed automated MCC site detection algorithms and utilities that are increasingly capable, achieving accurate classification of a high percentage of true positives (TPs), with accuracy levels above 0.90 and even as high as 0.95 in some cases. Similarly, it is desirable to minimize the number of false positives (FPs) to no more than about 0.2 FPs per image processed. While the results that have been achieved by such CAD systems are impressive, however, some diagnosticians are requesting more; some diagnosticians prefer that CAD tools for MCC detection show virtually all MCC sites in order to be acceptable.
It is understood that MCC segmentation is one aspect of MCC classification. A popular approach toward MCC spot candidate segmentation is applying contrast enhancement to the digitized mammograms followed by image segmentation and classification procedures (K. Thangavel, M. Karnan, R. Sivajumar and A. Mohideen, “Automatic detection of microcalcification in mammograms—a review,” Int. J. on Graphics, Vision and Image Processing, 5 (5) 31-36 (2005)). Bocchi et al. use a fractal model to describe the mammography image, allowing the use of a matched filtering stage to enhance MCC against the background. Image segmentation is carried out by growing connected components of the filtered image after zero thresholding (L. Bocchi, G. Coppini, J. Nori and G. Valli, “Detection of single and clustered microcalcifications in mammograms using fractals models and neural networks,” Med. Imaging & Phys., 24, 303-312 (2004)). Ge et al. employ a difference-image technique using an 8×8 box-rim filter to enhance signal to noise ratio of the MCCs. A global thresholding procedure is then used to segment the individual MCC candidates from the difference image (J. Ge, L. Handjiiski, B. Sahiner, J. Wei, M. Helvic, C. Zhou and H. Chan, “Computer-aided detection system for clustered microcalcifications: comparison of performance on full-field digital mammograms and digitized screen-film mammograms,” Phys. Med. Biol., 52, 981-1000 (2007)). Kang, Ro and Kim in their paper (H. Kang, Y. Ro and S. Kim, “A microcalcification detection using adaptive contrast enhancement on wavelet transformation and neural network,” IEICE T. Inf & Syst. E89-Db B (3), 1280-1287 (2006)) introduce an image enhancement method by utilizing noise characteristics to change the parameters in homomorphic filtering that decreases the energy of low frequencies while increasing that of high frequencies in the image. The homomorphic filter is applied to wavelet coefficients after performing wavelet transformation of the mammographic image.
There are publications that describe methods of MCC spot candidate segmentation without explicit image enhancement procedures. Halkiotis et al. (S. Halkiotis, T. Botsis and M. Rangoussi, “Automatic detection of clustered microcalcifications in digital mammograms using mathematical morphology and neural networks,” Signal Processing, 87, 1559-1568 (2007)) consider each mammogram as a topographic representation, each MCC as an elevation constituting a regional maximum. A morphological operation (geodesic dilation and geodesic erosion) is applied to remove noise and regional maxima that do not correspond to calcifications. Sankar and Thomas use deterministic fractal objects to model the breast background tissues based on the mean and variance of the image blocks. Microcalcification spot candidates can be segmented by taking the difference between the original image and the modeled image (D. Sankar and T. Thomas, “Fractal modeling of mammograms based on mean and variance for the detection of MCCs,” Proc. Int. Conf. Computational Intelligence and Multimedia Applications, 334-338 (2007)). Hirako et al. consider the MCC spot as having a circular cone. They use a triple-ring filter to extract features using the image gradient information to segment MCC spots (K. Hirako, H. Fujita and T. Hara, “Development of detection filter for microcalcifications on mammograms: a method based on density gradient and triple-ring filter analysis,” Systems and Computers in Japan, 27 (13), 36-48 (1996)). Hirako's method explores the underlying directional information of the MCC spots. This approach, however, is disadvantaged, often introducing noise to the processed image.
There is a need for improved performance and accuracy in CAD utilities that provide MCC detection. However, even with improved segmentation techniques, more capable image processing software, more powerful computing hardware, and continuing work on image classifiers, the goal of achieving near-100% accuracy remains elusive.